Free eBooks On Machine Learning | Hacker News Reader

Free eBooks On Machine Learning

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123 pointsmhausenblas16 comments

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The LION Way: Machine Learning plus Intelligent Optimization http://www.e-booksdirectory.com/details.php?ebook=9575

A Course in Machine Learning http://www.e-booksdirectory.com/details.php?ebook=9395

A First Encounter with Machine Learning http://www.e-booksdirectory.com/details.php?ebook=8818

Bayesian Reasoning and Machine Learning http://www.e-booksdirectory.com/details.php?ebook=5283

Introduction to Machine Learning http://www.e-booksdirectory.com/details.php?ebook=4493

The Elements of Statistical Learning: Data Mining, Inference, and Prediction http://www.e-booksdirectory.com/details.php?ebook=3267

Reinforcement Learning by C. Weber, M. Elshaw, N. M. Mayer http://www.e-booksdirectory.com/details.php?ebook=3227

Machine Learning by Abdelhamid Mellouk, Abdennacer Chebira http://www.e-booksdirectory.com/details.php?ebook=2852

How Are We To Know? by Nils J. Nilsson http://www.e-booksdirectory.com/details.php?ebook=2710

Reinforcement Learning: An Introduction http://www.e-booksdirectory.com/details.php?ebook=1825

Gaussian Processes for Machine Learning http://www.e-booksdirectory.com/details.php?ebook=1774

Machine Learning, Neural and Statistical Classification http://www.e-booksdirectory.com/details.php?ebook=1118

Introduction To Machine Learning http://www.e-booksdirectory.com/details.php?ebook=1117

Inductive Logic Programming: Techniques and Applications http://www.e-booksdirectory.com/details.php?ebook=1105

Practical Artificial Intelligence Programming in Java http://www.e-booksdirectory.com/details.php?ebook=32

Information Theory, Inference, and Learning Algorithms http://www.e-booksdirectory.com/details.php?ebook=21

The bad part about eBooks is that they always pile up. They are probably the most non-read books in existence. Or why should I bother reading 16 eBooks on the same topic, when reading a single good one would be the sane solution (the one I'd choose for paper books)?

I certainly agree with you. Often, it's just better to buy a paper book than try to read a little bit here and there. After all, paper books are not that expensive. My personal problem is than I often buy books that I never end up reading, or that I read after years (five, six, or even more). I'm pretty sure I'm not the only one that suffers from this, though.

Actually there are several really good books in this list, also available as paper copies if you want (albeit quite expensive if you go that route. ESL for example is $70+).

But I do agree, a good textbook is well worth the investment over some free poorly written one.

ebooks are brilliant as reference books. You can search and access them on multi devices almost every where.

Interesting.

I didn't knew that the Hastie/Tibshirani/Friedman was legally available as a free download. I would recommend it to anyone with a sufficient maths/stats background.

Yes, both 'The Elements of Statistical Learning' and 'An Introduction to Statistical Learning with Applications in R' are available free in pdf.

For fans of hard copy, I recently found that if your local (university?) library is a SpringerLink customer, you can purchase a print-on-demand copy of either book for $26.99, which includes shipping. Interior pages are in black and white (including the graphs), but that is a really cheap price for these two.

Andrew Ng's course notes from his physical class at Stanford (CS 229 - Machine Learning) are extensive and available as well at:

http://cs229.stanford.edu/materials.html

Dangerous links (Google ad network masquerading as downloads of the content.)

First listing is from a commercial solver, rather sales oriented, though it looks like the topics may not depend on it?

i'd suggest following prof ng's free online course instead, just finished it a few weeks ago and it's really good!

https://class.coursera.org/ml-004/

I'd suggest going back to the original youtube[1] and course materials[2]. Coursera version is nothing but a hand-wavy watered down "feel good" version of the original class. I also really like the Caltech's take "Learning from data"[3]

[1] https://www.youtube.com/watch?v=UzxYlbK2c7E

[2] http://cs229.stanford.edu/

[3] http://work.caltech.edu/telecourse.html

sorry but i disagree. the in video questions, the randomised problem sets, all the coursera stuff really helped me to learn the ideas.

While not free, 'Machine Learning: A Probabilistic Perspective' (http://www.amazon.co.uk/gp/aw/d/0262018020) is the best book I have found so far. I also second the recommendations for Tibshirani's and MacKay's books; the former for mathematical foundations, the latter for the intuition.

http://www.amazon.com/Programming-Collective-Intelligence-Bu...

I found this to be quite a good introduction.

I also have this book and highly recommend it.

First of all thanks for sharing. I would like to study machine learning. I have a good code and math background. Which of these books is the most recommended?

"The Elements of Statistical Learning" is great. It assumes an astute reader, but if you've made it through some post-graduate level work, you'd be fine.

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